An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems. (1st June 2020)

• Record Type: Journal Article
• Authors: Rizzo, Lucas; Longo, Luca
• Is Part Of: Expert systems with applications. Volume 147(2020)
• Publication Date: 2020-06-01
• Subjects: Defeasible argumentation -- Argumentation theory -- Explainable artificial intelligence -- Non-monotonic reasoning -- Fuzzy logic -- Expert systems -- Mental workload; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113220
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Replicable comparison of inferences produced by non-monotonic reasoning approaches. Assessment of the ill-defined construct of mental workload using real-world data. Defeasible argumentation presented a superior inferential capacity of mental workload. Use of defeasible argumentation in practical fields seldom reported in the literature. Robust results analysed in two real-world contexts with three knowledge bases. Abstract: Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined construct, and its formalisation can be seen as a reasoning activity under uncertainty. An experimental work was performed by exploiting three deductive knowledge bases produced with the aid of experts in the field. These were coded into models by employing the selected techniques and were subsequently elicited with data gathered from humans. The inferences produced by these models were in turn analysed according to common metrics of evaluation in the field of mental workload, in specific validity and sensitivity. Findings suggest that the variance of the inferences of expert systems and fuzzy reasoning models was higher, highlighting poor stability. Contrarily, that of argument-based models was lower, showing a superior stability of its inferences across knowledge bases and under different system configurations. The originality of this research lies in the quantification of the impact of defeasible argumentation. It contributes to the field of logic and non-monotonic reasoning by situating defeasible argumentation among similar approaches of non-monotonic reasoning under uncertainty through a novel empirical comparison.